Type 2 Diabetes Mellitus (T2DM) is a growing healthcare problem in the United States that increases the risk for numerous health complications if left unidentified and untreated. Prediabetes, while not a clinical diagnosis, is a state of increased risk of developing T2DM based on elevated blood glucose laboratory markers such as hemoglobin A1C (HbA1C). There are numerous risk factors that predispose individuals to prediabetes and T2DM. Researchers have shown that targeting those risk factors that are modifiable, such as physical inactivity and obesity, with exercise and diet interventions can increase physical activity, decrease weight, decrease HbA1C, and decrease the incidence of T2DM in prediabetics. Tools such as pedometers that track physical activity in the form of step count can be used in interventions to improve upon these metrics. Researchers have also shown that pedometers can enhance interventions aimed at improving physical activity, weight, HbA1C, and incidence of T2DM. Recently, electronic tools that are wearable such as the Fitbit Flex2 have gained in popularity due to their additional interactive features with users. These electronic wearable devices employ behavior change techniques approved by the US Preventive Services Task Force to motivate individuals to be more physically active. Current research has shown that these electronic wearable devices enhance interventions aimed at improving physical activity, weight loss, and HbA1C in those with T2DM. Yet, there is a gap in current research that examines the effect that these devices have on HbA1C in prediabetics. The proposed study seeks to examine if the Fitbit Flex 2 wrist device, in conjunction with a standard diet and exercise intervention, improves HbA1C measures in prediabetic individuals over a one-year period. Results from the proposed study could support the future use of these devices to help decrease HbA1C measures and the risk of development of T2DM and other T2DM- health complications in prediabetics. Electronic wearable devices could alter the way in which clinicians monitor lifestyle interventions aimed at T2DM risk reduction and treatment. The use of electronic wearable devices may also serve as a more cost effective treatment alternative for those at risk of developing, as well as those diagnosed with, T2DM.

Technological innovations have lead to the development of Wearable Physiological Monitoring devices, that have enabled researchers and clinicians in real-time monitoring of physiologic function within a field setting. However, it is important to establish the psychometric properties of a device prior to its utilization. A systematic review was conducted to provide a summary and appraise the quality of the literature on psychometric parameters of Zephyr Bioharness and Fitbit devices. Based on this review, we addressed the current gaps in the literature regarding the reliability parameters of Zephyr Bioharness and Fitbit Charge devices, and established the validity and agreement properties of Fitbit Charge device. For our systematic review, we searched the Google Scholar and PubMed databases to identify articles. To establish the reliability, validity and agreement parameters of Zephyr Bioharness and Fitbit Charge devices, a convenience and snowball sampling approaches were used to recruit sixty participants (30 females) from university student, staff, faculty population, and MacSeniors Community Program at McMaster University. The performance of Zephyr and Fitbit devices were assessed throughout three phases; rest, Modified Canadian Aerobic Fitness Test and recovery. In our study, at rest, inter-session average heart rate (beats/min.) ICCs (SEM) for Zephyr and Fitbit ranged from 0.90 – 0.94 (1.73 – 2.37) and 0.88 – 0.94 (1.83 – 2.67) respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91 – 0.97 (3.12 – 4.64) and 0.85 – 0.98 (3.28 – 4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93 – 0.97 (2.65 – 4.66) and 0.76 – 0.91 (3.17 – 4.67) for Zephyr and Fitbit devices respectively. Pearson’s correlation coefficients and (Mean differences) for heart rate variable were 0.97 – 0.99 (-0.60 – 0.02) at Rest, 0.89 – 0.99 (13.51 – 0.62) at submaximal testing and 0.70 – 0.84 (-0.54 – 2.52) throughout recovery. The average agreement bias of heart rate in pair-wise device comparison indicated mean differences of -0.20, 4.00 and 1.00 at rest, sub-maximal testing and recovery respectively. We identified fair to very good quality evidence from 14 studies. The Zephyr Bioharness and Fitbit Charge devices demonstrated excellent reliability measures, and the Fitbit Charge device heart rate variable demonstrated strong to very strong correlations when concurrently compared with Zephyr, and provided valuable information regarding its interchangeable use in a sample of sixty healthy male and female participants of various age groups during a resting, standardized submaximal fitness and recovery phases. / Thesis / Master of Science (MSc)

BACKGROUND: Prior to now, sleep has been particularly difficult to accurately measure in a home setting. The acceptability of wearable devices that allow for the collection of not only sleep data, but heart rate, activity, and other variables allows for the expansion of research from the lab into a patient’s daily life. METHODS: Data from three studies was evaluated to determine acceptability of Fitbit wearable devices as well as the utility of sleep data the devices could collect to characterize sleep disturbances. The populations included two post-surgical groups experiencing acute pain (orthopedic surgery, n = 7; cardiac surgery n = 14) and one group of children and adolescents receiving intensive treatment for chronic pain (n = 14). RESULTS: Both the composition of the studies and the Fitbit devices were well tolerated by all three groups, as measured by an acceptability survey. For utility, significant differences were found in step counts, number of nightly awakenings, and pain ratings. Correlations were also found between pain and sleepiness, step counts, and sleep efficiency. CONCLUSION: The future is bright for the integration of wearable devices and other smart technology into clinical settings. For the populations studied here, this could mean fewer hospital visits and increased quality of life by being able to provide feedback to providers via validated, non-invasive methods.

<p dir="ltr">Wearable devices are ubiquitous. There are over 1.1 billion wearable devices in the<br>market today[1]. The market is projected to grow at a rate of 14.6% annually till 2030[2].<br>These devices collect and store a large amount of data[3]. A major amount of this collected<br>data is stored in the cloud. For many years now, law enforcement organizations have been<br>continuously encountering cases that involve a wearable device in some capacity. There have<br>also been examples of how these wearable devices have helped in crime investigations and<br>insurance fraud investigations [4],[5],[6],[7],[8]. The article [4] performs an analysis of 5 case<br>studies and 57 news articles and shows how the framing of wearables in the context of the<br>crimes helped those cases. However, there still isn’t enough awareness and understanding<br>among law enforcement agencies on leveraging the data collected by these devices to solve<br>crimes. Many of the fitness trackers and smartwatches in the market today have more or<br>less similar functionalities of tracking data on an individual’s fitness-related activities, heart<br>rate, sleep, temperature, and stress [9]. One of the major players in the smartwatch space is<br>Fitbit. Fitbit synchronizes the data that it collects, directly to Fitbit Cloud [10]. It provides<br>an Android app and a web dashboard for users to access some of these data, but not all.<br>Application developers on the other hand can make use of Fitbit APIs to use user’s data.<br>These APIs can also be leveraged by law enforcement agencies to aid in digital forensic<br>investigations. There have been previous studies where they have developed tools that make<br>use of Fitbit Web APIs [11],[12], [13] but for various other purposes, not for forensic research.<br>There are a few studies on the topic of using fitness tracker data for forensic investigations<br>[14],[15]. But very few have used the Fitbit developer APIs [16]. Thus this study aims to<br>propose a proof-of-concept platform that can be leveraged by law enforcement agencies to<br>access and view the data stored on the Fitbit cloud on a person of interest. The results<br>display data on 12 categories - activity, body, sleep, breathing, devices, friends, nutrition,<br>heart rate variability, ECG, temperature, oxygen level, and cardio data, in a tabular format<br>that is easily viewable and searchable. This data can be further utilized for various analyses.<br>The tool developed is Open Source and well documented, thus anyone can reproduce the<br>process.<br>12<br></p>

Background: The health benefits of regular physical activity are well known and include the prevention of chronic diseases such as obesity, type 2 diabetes, and cardiovascular diseases. Still, only 20% of U.S. adults report meeting the Physical Activity Guidelines for Americans. With approximately 43% of U.S. jobs considered sedentary, there is a need for effective workplace physical activity interventions. MapTrek is a mobile health game designed to increase daily physical activity in a low-cost, scalable, and enjoyable way. Objective: The purpose of the present study was to test the efficacy of MapTrek for increasing daily steps and moderate-intensity steps over 10 weeks in a sample of sedentary office workers. Methods: Participants included 144 full-time sedentary office workers ages 21-65 who reported sitting at least 75% of their workday. Each participant received a Fitbit Zip to wear daily throughout the intervention. Participants were randomized to either a: 1) Fitbit only group or 2) Fitbit + MapTrek group. Physical activity outcomes and intervention compliance were measured with the Fitbit activity monitor. Results: The Fitbit + MapTrek group significantly increased daily steps (+2,091.5 steps/day) and active minutes (+11.2 minutes/day) compared to the Fitbit only arm. Conclusions: These data support MapTrek as an effective approach for increasing physical activity at a clinically meaningful level in sedentary office workers.

Obesity is a problem of vast social concern in the United States. One factor that has been linked to reduction in body fat and the health problems associated with obesity is increasing physical activity. Although in-person behavioral interventions have been shown effective at increasing physical activity, attention is now being placed on disseminating these interventions through the use of technology. Several internet-based interventions have been developed and are readily available. The purpose of this study was to evaluate "Fitbit," a web-based behavioral intervention for increasing physical activity and losing weight. Additionally, this study examined if the addition of contact from a behavioral coach through videoconference and email enhanced the effectiveness of this program. Through a multiple-baseline design across seven participants this research project evaluated the effectiveness of the "Fitbit" program with and without a behavioral coach. Step counts were recorded by a Fitbit sensor as a measure of physical activity. The Fitbit program alone increased physical activity for some of the participants, and the addition of the behavioral coach resulted in further increases in mean step counts.

Physical inactivity is a widespread problem associated with numerous health problems. Individuals with intellectual disabilities are at a high risk of living a sedentary lifestyle. Although a few studies have examined interventions consisting of goal-setting and reinforcement for increasing PA, no studies have evaluated the use of monetary reinforcement. Interventions using monetary reinforcement have been shown to be effective for increasing PA with typically developing adults. The present studies evaluated monetary reinforcement for increasing PA in adults with intellectual disabilities. Study 1 evaluate a session-based intervention where participants earned monetary rewards for attaining step count goals as recorded by pedometers. The intervention increased the rate of walking for all five participants, demonstrating experimental control with four participants. The study also found that a staff member implemented the intervention with fidelity and rated it as highly acceptable. Study 2 evaluated a whole-day intervention where participants earned monetary rewards for attaining daily step goals as measured by wrist-worn Fitbit Alta™ accelerometers. The whole-day intervention resulted in noticeable increases in daily steps for only two participants, with experimental control demonstrated for one participant. Discussion includes the advantages and limitations of the approaches in each study and recommendations for future studies.

Obesity is a growing problem that has life-threatening health consequences. One way to combat obesity is by increasing physical activity levels, which has been a focus of recent applied behavioral research. The purpose of this study was to evaluate the effectiveness of goal setting and textual feedback without social support to increase physical activity, specifically weekly running distance. A multiple-baseline across participants design was employed with four participants using a Fitbit Flex accelerometer to collect two physical activity measures, intense steps and distance. Results showed a significant increase in weekly running distance for two out of four participants following the intervention.

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Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the whole system. In this thesis, we design and implement a system that is capable of finding the optimal set of data features to make private, given the device’s maximum resource constraints and the desired performance or accuracy of the system. Using the generalization techniques for data anonymization, we create user-defined injective privacy encoder functions to make each feature of the dataset private. Regardless of the resource availability, some data features are defined by the user as essential features to make private. All other data features that may pose privacy threat are termed as the non-essential features. We propose Dynamic Iterative Greedy Search (DIGS), a greedy search algorithm that takes the resource consumption for each non-essential feature as input and returns the most optimal set of non-essential features that can be private given the available resources. The most optimal set contains the features which consume the least resources. We evaluate our system on a Fitbit dataset containing 17 data features, 4 of which are essential private features for a given classification application. Our results show that we can provide 9 additional private features apart from the 4 essential features of the Fitbit dataset containing 1663 records. Furthermore, we can save 26:21% memory as compared to making all the features private. We also test our method on a larger dataset generated with Generative Adversarial Network (GAN). However, the chosen edge device, Raspberry Pi, is unable to cater to the scale of the large dataset due to insufficient resources. Our evaluations using 1=8th of the GAN dataset result in 3 extra private features with up to 62:74% memory savings as compared to all private data features. Maintaining privacy not only requires additional resources, but also has consequences on the performance of the designed applications. However, we discover that privacy encoding has a positive impact on the accuracy of the classification model for our chosen classification application. / Distribuerad databehandling möjliggör effektiv datalagring, bearbetning och hämtning men det medför säkerhets- och sekretessproblem. Sensorer är hörnstenen i de IoT-baserade rörledningarna, eftersom de ständigt samlar in data tills de kan analyseras på de centrala molnresurserna. Dessa sensornoder begränsas dock ofta av begränsade resurser. Helst är det önskvärt att göra alla insamlade datafunktioner privata, men på grund av resursbegränsningar kanske det inte alltid är möjligt. Att göra alla funktioner privata kan orsaka överutnyttjande av resurser, vilket i sin tur skulle påverka prestanda för hela systemet. I denna avhandling designar och implementerar vi ett system som kan hitta den optimala uppsättningen datafunktioner för att göra privata, med tanke på begränsningar av enhetsresurserna och systemets önskade prestanda eller noggrannhet. Med hjälp av generaliseringsteknikerna för data-anonymisering skapar vi användardefinierade injicerbara sekretess-kodningsfunktioner för att göra varje funktion i datasetet privat. Oavsett resurstillgänglighet definieras vissa datafunktioner av användaren som viktiga funktioner för att göra privat. Alla andra datafunktioner som kan utgöra ett integritetshot kallas de icke-väsentliga funktionerna. Vi föreslår Dynamic Iterative Greedy Search (DIGS), en girig sökalgoritm som tar resursförbrukningen för varje icke-väsentlig funktion som inmatning och ger den mest optimala uppsättningen icke-väsentliga funktioner som kan vara privata med tanke på tillgängliga resurser. Den mest optimala uppsättningen innehåller de funktioner som förbrukar minst resurser. Vi utvärderar vårt system på en Fitbit-dataset som innehåller 17 datafunktioner, varav 4 är viktiga privata funktioner för en viss klassificeringsapplikation. Våra resultat visar att vi kan erbjuda ytterligare 9 privata funktioner förutom de 4 viktiga funktionerna i Fitbit-datasetet som innehåller 1663 poster. Dessutom kan vi spara 26; 21% minne jämfört med att göra alla funktioner privata. Vi testar också vår metod på en större dataset som genereras med Generative Adversarial Network (GAN). Den valda kantenheten, Raspberry Pi, kan dock inte tillgodose storleken på den stora datasetet på grund av otillräckliga resurser. Våra utvärderingar med 1=8th av GAN-datasetet resulterar i 3 extra privata funktioner med upp till 62; 74% minnesbesparingar jämfört med alla privata datafunktioner. Att upprätthålla integritet kräver inte bara ytterligare resurser utan har också konsekvenser för de designade applikationernas prestanda. Vi upptäcker dock att integritetskodning har en positiv inverkan på noggrannheten i klassificeringsmodellen för vår valda klassificeringsapplikation.